1. Field of the Invention
The invention concerns a method to acquire a measurement data set of a breathing examination subject by means of magnetic resonance technology; a magnetic resonance system; a computer program; and an electronically readable data medium.
2. Description of the Prior Art
Magnetic resonance (MR) is a known technology with which images from the interior of an examination subject can be generated. Expressed simply, the examination subject is placed in a magnetic resonance imaging scanner, in a strong, static, homogenous base magnetic field, also called a B0 field, having a field strength of 0.2 tesla-7 tesla and more, such that the nuclear spins of the subject orient themselves along the base magnetic field. In order to trigger magnetic resonance signals, the examination subject is irradiated with high frequency excitation pulses (RF pulses), the triggered magnetic resonance signals are detected and entered into a memory that represents a mathematical domain known as k-space, and MR images are reconstructed on the basis of the k-space data, or spectroscopy data are determined. For the spatial encoding of the measurement data, rapidly activated magnetic gradient fields are superimposed on the base magnetic field. The recorded measurement data are digitized and stored as complex number values in a k-space matrix. From the k-space matrix populated with data values in this manner, an associated MR image can be reconstructed, for example, by means of a multi-dimensional Fourier transformation.
The respiratory movement of a patient that is to be examined by means of MR can lead to so-called ghosting, to blurring, and/or to intensity losses in the images generated, as well as registration errors between generated images particularly in an examination of the organs of the thorax and the abdomen, i.e. of examination regions affected by respiratory movement. These artifacts can make it difficult for a physician to perform an analysis on the basis of the images, and can lead to lesions being overlooked, for example. Numerous techniques exist in the prior art for reducing artifacts resulting from respiratory movement. One of these techniques is respiratory gating. Respiratory gating is a technique with which, during the MR measurement, the respiration of the patient is recorded and assigned to the acquired measurement data. With respiratory gating, only measurement data are then used for reconstruction for which the associated recorded respiratory movement fulfills certain specifiable criteria.
The breathing of the patient can be detected with external sensors, for example a pneumatic cushion, or with MR signals (known as navigators). A navigator is normally a short sequence that acquires MR signals, for example of the diaphragm or another signal source in the examination subject whose movement is correlated with the breathing of the patient. The breathing movement can be reconstructed via the position of the diaphragm or the other signal source.
In breath gating with navigators, the navigator sequence is (for example) interleaved with the imaging sequence, and a diaphragm position measured with a navigator is subsequently associated with the imaging data acquired immediately following (or before) this.
A distinction is made between retrospective and prospective respiratory gating.
With retrospective respiratory gating the respiratory movement is detected and recorded during the MR measurement, but not evaluated. Instead, the k-space that is to be recorded is measured repeatedly. For the reconstruction, only a portion of the measured data are referenced, preferably that data in which the respiratory signal lies within a specific window for a distinctive respiratory position. If a specific k-space data point that is necessary for the image reconstruction is repeatedly measured within the distinctive window, then the data can be averaged. If, instead, a data point is always measured outside of the window, then that data point deviating the least from the distinctive position can be used for the reconstruction.
With prospective respiratory gating, the physiological respiratory signal measured using a respiratory sensor (e.g. the diaphragm position measured with a navigator sequence) is evaluated during the measurement, and the MR measurement is controlled, based on the recorded physiological signal. In the simplest embodiment, the so-called acceptance/rejection algorithm (ARA), the measurement of an imaging data packet (and if applicable, the associated navigator sequence) is repeated until the physiological signal falls within a previously defined acceptance window.
One example of an acceptance/rejection algorithm of this type and, at the same time, the first description of respiratory gating with navigators, is described in the article by Todd S. Sachs, Craig H. Meyer, Bob S. Hu, Jim Kohli, Dwight G. Nishimura and Albert Macovski: “Real-Time Motion Detection in Spiral MRI Using Navigators,” MRM 32: Pages 639-645 (1994). The authors acquired one or more navigators for each excitation of a spiral sequence. The navigators were acquired here following the acquisition of the image data. Different navigators are distinguished by their spatial orientation. From each navigator, a spatial displacement along the axis of the navigator in relation to a reference navigator is calculated using a cross-correlation. The navigator scan acquired following the first imaging scan is used, in each case, as a reference. A specific imaging scan is repeated until the spatial displacement determined with the navigator, in relation to the reference, is less than a threshold value provided by a user. This, therefore, relates to an acceptance/rejection algorithm based on one or more spatial displacements.
Another example of an acceptance/rejection algorithm is described by Wang et al. in “Navigator-Echo-Based Real-Time Respiratory Gating and Triggering for Reduction of Respiratory Effects in Three-Dimensional Coronary MR Angiography,” Radiology 198; Pages 55-60 (1996). In this case, the physiological signal is the displacement of the diaphragm position, determined with a navigator, in relation to a reference state. One difference from the work by Sachs et al. is that, in each case, a navigator is acquired before and after the imaging scan, and that the imaging scan is then only accepted if the displacement determined by means of both navigators is less than the threshold value.
In order to determine the acceptance window, a so-called pre-scan is normally carried out for each patient, in which the respiratory movement is recorded, for example, with the navigator sequence, but imaging data are not yet acquired.
Prospective respiratory gating is normally more efficient than retrospective respiratory gating. A prerequisite for prospective respiratory gating is a real-time capability of the normally-provided control software for the MR apparatus. For this purpose, real-time capability means that data measured with the sequence (in this case, the sequence comprises imaging and navigator sequences) can be evaluated during the sequencing, and the further course of the sequencing can be influenced by the results of this evaluation, wherein the time period between recording the data and influencing the further course is short in comparison with the typical time constants of the respiratory movement (in this case, particularly, the respiratory cycle of a human being, which amounts to between 3 and 10 seconds).
The main problem with the acceptance/rejection algorithm is that the respiration of the patient frequently varies during the examination. The variations in the respiratory movement can be such that the respiratory positions within the once specified acceptance window are rarely, or no longer, detected. This leads to extended acquisition periods and can even lead to the measurement not being completed at all in the normal manner.
The most important algorithm, by far, that addresses this problem is “Phase Ordering With Automatic Window Selection” (PAWS), which is described, for example, in the article by P. Jhooti, P. D. Gatehouse, J. Keegan, N. H. Bunce, A. M. Taylor, and D. N. Firmin, “Phase Ordering With Automatic Window Selection (PAWS): A Novel Motion-Resistant Technique for 3D Coronary Imaging,” Magnetic Resonance in Medicine 43, Pages 470-480 (2000) and in the US patent, U.S. Pat. No. 7,039,451 B1. PAWS finds a final acceptance window during the runtime, and can thus react in a flexible manner to a changing respiration. A further goal of PAWS is to ensure a certain degree of “phase-encode ordering” (or in short, “phase ordering”). This means that adjacent lines in the k-space are acquired in similar respiration states. In particular, a variation in the respiratory state during acquisitions in the vicinity of the k-space center, which is particularly sensitive to movement, is to be avoided. PAWS was developed for a 3D Cartesian acquisition technique. The ky-kz array system used for this acquires a complete kx-kz plane of the 3-dimensional k-space following each navigator. The modulation of the k-space signal along the kz axis resulting from the transcendental state after interrupting the stationary steady state by the navigator (as well as potential activated preparation pulses, or the waiting for a further physiological signal, such as an EKG trigger) on the kx-kz plane, is therefore smooth. Discontinuations may arise in the ky axis as a result of residual movement, which can be manifested in the image as artifacts and blurring along the first phase encoding axis ky. This does not only apply when the segment border exists in the vicinity of the k-space center. Peristaltic movements, as well, which are not detected by the respiratory sensor, can lead to artifacts in the images.
PAWS exists in different variants, known as “bin” variants. In PAWS, the width of the final acceptance window is established. In contrast to the acceptance/rejection algorithm, the breathing positions that this acceptance window includes are automatically found at run time. The k-space filling takes place in clusters. A cluster (in the original work the term “bin” was used instead of cluster) is characterized by a breathing position range (an acceptance range) and includes all k-space lines that have already been measured after a breathing position has been measured in the breathing position range associated with the cluster. In the n-bin variant of PAWS, a breathing position range whose width is equal to the acceptance window is covered by n successive clusters.
Furthermore, a starting position in the k-space is assigned to each cluster, wherein the number of different starting positions is n. Different starting positions are assigned to clusters with adjacent respiratory positions where n>1. As soon as a respiratory position assigned to a cluster is measured with the navigator, the measurement of a k-space line that has not yet been measured within said cluster is initiated. The decision regarding which k-space lines still to be measured are selected takes into consideration, as a whole, the already acquired k-space lines of adjacent clusters as well. By way of example, a still missing k-space line is selected such that an arbitrary group of n adjacent clusters is complete to the greatest degree possible, wherein the arbitrary group of n adjacent clusters contains the cluster to which the current measured respiratory position is assigned; i.e. the group of n adjacent clusters comprising the largest possible number of different k-space lines. As soon as an arbitrary group of n adjacent clusters comprises all of the k-space lines that are to be measured, the measurement is stopped, because the overall variation in the respiratory position is limited in these measurement data, thereby, to the acceptance window.
The n different starting points and clusters of the n-bin variation of PAWS normally result in n segments in the k-space. For this, each segment consists of adjacent k-space lines. The variations to the respiratory positions within a segment measured with the navigator correspond to the position range assigned to a cluster (in the original work, the term “bin size” is used), and thus one nth of the acquisition window. The variation to the respiratory position is greater over the course of the entire k-space, and has an upper limit as a result of the specified acceptance window. The lines belonging to the same segment are measured during similar respiratory states. Thus, the modulation of the signal changes with the respiration at the segment borders. As a result, position jumps occur at the segment borders. An aim of the different bin-variations of PAWS is to displace the segment borders away from the movement sensitive k-space center. Another aim is to obtain a greater degree of efficiency.
In the previously mentioned article by Jhooti et al., as well as in the follow-up work by P. Jhooti, P. Gatehouse J. Keegan, A. Stemmer, D. Firmin: “Phase ordering with Automatic Window Selection (PAWS) with Half Fourier for Increased Scan Efficiency and Image Quality;” Proc. Intl. Soc. Mag. Reson. Med. 11 (2004); Page 2146, the 1-bin, 2-bin, 3-bin, and 4-bin variations are compared with one another. The result of this comparison shows that the 1-bin and the 2-bin variations of PAWS are the most efficient, i.e. for a given width of the acceptance window, the measurements are completed most quickly. The 1-bin variation is discarded because it does not allow for “phase ordering,” the 4-bin variation (and higher) is discarded due to lower efficiency. The 3-bin variation is less efficient than the 2-bin variation. The reason for this is the unidirectional growth direction of the cluster with starting positions at the left and right k-space edges. As soon as the gap between one of these peripheral clusters and the central cluster (with a starting position in the k-space center, and a bidirectional growth direction) is closed, then said clusters continue to grow until the gap between the other peripheral clusters and the central cluster is closed, as soon as a respiratory position is measured that is assigned to the first peripheral cluster. This normally leads to multiple k-space lines acquired at the cluster borders (segment borders). This problem does not exist with the 2-bin variation. In this variation, every second cluster grows in a unidirectional manner from the left-hand k-space edge, through the k-space center, toward the right-hand k-space edge, and the remaining clusters grow in a unidirectional manner from the right-hand k-space edge, through the k-space center, toward the left-hand k-space edge. The measurement is complete as soon as two adjacent clusters (with opposite growth directions) “meet.” However, with a symmetrical scanning of the k-space, as is the case with the 2-bin variation, the cluster border frequently lies in the vicinity of the k-space center, which is particularly sensitive to movement, which may lead to strong image artifacts. The probability of cluster borders lying in the vicinity of the k-space center is substantially lower with the use of partial Fourier (i.e. an asymmetric scanning of the k-space).
Of practical relevance, therefore, are the so-called 2-bin and 3-bin versions of PAWS, wherein, with symmetrical scanning, the 3-bin variation is preferred, and with asymmetric scanning, the 2-bin variation is preferred. This analysis is based on a 2-bin variation, in which the starting position alternates between the left-hand and right-hand k-space edges of adjacent clusters. Accordingly, the clusters grow, respectively, from the starting positions assigned thereto, firstly toward the k-space center.
It is noted again that only a single breathing position is associated with a cluster in some jobs. The width of the final acceptance window then amounts to n-times the resolution of the breathing signal. In this alternative formulation, a more flexible selection of the acceptance window is achieved in that the breathing position measured with the sensor is initially coarsened, such that n-adjacent resulting breathing positions cover a breathing range that corresponds to the width of the acceptance window.
Three modifications of the 3-bin PAWS algorithm are known from Nuval et al., “An improved real-time navigator gating algorithm for reducing motion effects in coronary magnetic resonance angiography”; Journal of X-Ray Science and Technology 11 (2003), P. 115-123 and A. Nuval et al., “Refined PAWS Algorithms for 3D Coronary MRA”. Proc. Intl. Soc. Mag. Reson. Med. 11 (2003), P. 1625:
a) In the original 3-bin PAWS variant, clusters with start position at the left k-space edge, in the k-space center and at the right k-space edge alternate cyclically. In the modified version, the start position alternates cyclically between left k-space edge, in the k-space center, right k-space edge and k-space center again. A start position in the k-space center is accordingly assigned to every second cluster. Position jumps at the cluster boundaries that are twice as large as the acceptance range assigned to a cluster are avoided with this modification. However, this modification also reduces the number of cluster combinations in which k-space can be completed. The efficiency is thus reduced.
b) The termination criterion is tightened such that the central cluster must have acquired at least 30% of k-space symmetrically around the k-space center. The goal of this modification is to avoid cluster boundaries near the k-space center. This modification also extends the measurement time in general, and therefore reduces the efficiency.
c) A histogram of the occurring breathing positions is created with the aid of a prescan. The breathing position occurring most frequently during the prescan is assigned to a central cluster. This modification also reduces the probability of a segment boundary near the k-space center. However, the efficiency is reduced further by the prescan that is now necessary. Moreover, the information obtained with the aid of a prescan can only be transferred to the actual scan in the case of a regular respiration. The integration of prescan information with the actual PAWS therefore runs contrary to the idea of being robust with regard to changing breathing patterns.
PAWS was originally developed for a ky-kz ordering scheme in which all k-space lines are respectively acquired with a defined value of the second phase coding gradient (in the direction of kz) after acquisition of the breathing signal. The “phase ordering” is accordingly also limited to a Cartesian k-space direction, which can lead to intensified remaining movement artifacts in this direction.
In a recent article, PAWS is combined with a known Radial Phase Encoding (RPE) scheme (Christoph Kolbitsch, Claudia Prieto, Jouke Smink and Tobias Schaeffter: “Highly Efficient Whole-Heart Imaging Using Radial Phase Encoding-Phase Ordering With Automatic Window Selection”; Magnetic Resonance in Medicine 66 (2011); P. 1008-1018). The respective data acquired after a navigator thereby respectively have the same movement sensitivity. A special 2-bin scheme is implemented. In the one bin set, radial spokes in k-space are acquired in the clockwise direction; in the other bin set, they are acquired in the counter-clockwise direction. The goal of this scheme is to be able to repeatedly reconstruct the region of interest (ROI) in different breathing phases.